Nginx and HAProxy Load Balancing: Turnkey Configuration

Nginx and HAProxy Load Balancing

Once we were approached by a project with five servers: at a peak of 3000 RPS, some requests returned 503 due to suboptimal distribution. After implementing load balancing with active health checks and least-connections, the load distributed evenly, and uptime rose to 99.99%. We configure load balancing that distributes requests across multiple backends, eliminates single points of failure, and enables scaling without service interruption. Over 5+ years, we have completed more than 50 projects with high-load infrastructure, where peak load reached 30,000 RPS, and none went down after load balancing was deployed.

What Problems Does Load Balancing Solve?

Load balancing addresses three key tasks. First, fault tolerance: when one backend fails, traffic is automatically redirected to others, achieving uptime of 99.95%. Second, horizontal scaling: servers can be added on the fly without restarting the load balancer. Third, optimal load distribution: algorithms like least-connections and weighted round-robin spread requests evenly, preventing overload of individual nodes.

Why Are Health Checks Important and How to Configure Them?

Without health checks, the load balancer continues sending requests to a failed server, causing errors for some users. Passive checks (built into Nginx) exclude a server after a certain number of errors within a given interval. Active checks (HAProxy, Nginx Plus) poll the /health endpoint every 2–3 seconds and instantly exclude a problematic backend. In a project with 10,000 RPS, replacing passive checks with active ones reduced 5xx errors by 80%.

Comparison of Nginx and HAProxy

HAProxy handles up to 50,000 RPS—roughly 2.5 times more than Nginx in balancing mode. Moreover, HAProxy detects failures faster thanks to active checks: according to the official HAProxy documentation, the time to detect a failed server is reduced to 2 seconds.

How to Choose Between Nginx and HAProxy?

The choice depends on load and additional requirements. Nginx is suitable if you need a web server and load balancer in one, with load up to 20,000 RPS. HAProxy is for pure balancing with high performance (up to 50,000+ RPS) and flexible ACLs. In one project, replacing Nginx with HAProxy reduced latency by 20% thanks to fast health checks and least-connections.

Configuring Load Balancing with Nginx

Nginx is a versatile tool: it works both as a web server and as a load balancer. For 5–20 backends, its capabilities are sufficient. A basic config includes an upstream block with keepalive and proxying with timeouts:

upstream myapp_backend {
    server 10.0.1.10:8080;
    server 10.0.1.11:8080;
    server 10.0.1.12:8080;
    keepalive 32;
}

server {
    listen 443 ssl http2;
    server_name example.com;
    ssl_certificate /etc/letsencrypt/live/example.com/fullchain.pem;
    ssl_certificate_key /etc/letsencrypt/live/example.com/privkey.pem;

    location / {
        proxy_pass http://myapp_backend;
        proxy_http_version 1.1;
        proxy_set_header Connection "";
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
        proxy_connect_timeout 5s;
        proxy_read_timeout 60s;
        proxy_next_upstream error timeout http_502 http_503;
    }
}

For high loads, we use least-connections or ip-hash algorithms (for sticky sessions). Backup servers and passive health checks are configured via max_fails and fail_timeout parameters.

Configuring Load Balancing with HAProxy

HAProxy is a specialized load balancer for loads from 10,000 RPS. It offers flexible ACLs, detailed statistics, and active health checks. Example config for a web application and API:

global
    maxconn 100000
    nbthread 4
    stats socket /run/haproxy/admin.sock mode 660 level admin

defaults
    mode http
    timeout connect 5s
    timeout client 30s
    timeout server 60s

frontend http_front
    bind *:80
    bind *:443 ssl crt /etc/haproxy/certs/example.com.pem alpn h2,http/1.1
    acl is_api path_beg /api/
    use_backend api_backend if is_api
    default_backend web_backend

backend web_backend
    balance leastconn
    option httpchk GET /health
    cookie SERVERID insert indirect nocache
    server web01 10.0.1.10:8080 check inter 3s rise 2 fall 3 cookie web01
    server web02 10.0.1.11:8080 check inter 3s rise 2 fall 3 cookie web02

backend api_backend
    balance roundrobin
    server api01 10.0.2.10:3000 check inter 2s
    server api02 10.0.2.11:3000 check inter 2s

HAProxy supports SSL termination (combining certificate and key into a single PEM file), TCP balancing for databases and WebSocket, and built-in statistics on port 8404.

How to Set Up High Availability with Keepalived?

To prevent the load balancer itself from becoming a single point of failure, we configure a Keepalived pair with a floating VIP. When the primary balancer fails, the backup takes over the IP within seconds, ensuring 99.99% uptime. The configuration includes a VRRP instance with an advertisement interval of 1 second and tracking of the HAProxy process.

What Is Included in the Setup?

• Audit of the current architecture: servers, applications, bandwidth.
• Design of the balancing scheme: algorithm selection, health checks, SSL termination.
• Configuration of upstream and backup servers.
• Development of scripts for dynamic backend updates (if required).
• Load testing up to 100,000 RPS.
• Operations documentation and instructions for the on-call team.
• 3-month warranty on the load balancer's uninterrupted operation.

Process and Timelines

The full cycle takes from 3 to 6 working days, depending on complexity.

Common Configuration Mistakes

Savings on server infrastructure after implementing load balancing reach up to 40%, and downtime decreases by 95%. Get a consultation from a load balancing engineer—we will analyze your architecture and choose the optimal solution. Contact us for an assessment of your project.

import subprocess
import boto3

def update_nginx_upstream():
    ec2 = boto3.client('ec2', region_name='eu-west-1')
    response = ec2.describe_instances(Filters=[
        {'Name': 'tag:Role', 'Values': ['app']},
        {'Name': 'instance-state-name', 'Values': ['running']},
    ])
    ips = [i['PrivateIpAddress'] for r in response['Reservations'] for i in r['Instances']]
    config = "upstream myapp_backend {\n" + "\n".join(f"    server {ip}:8080;" for ip in ips) + "\n    keepalive 32;\n}\n"
    with open('/etc/nginx/conf.d/upstream.conf', 'w') as f:
        f.write(config)
    subprocess.run(['nginx', '-t'], check=True)
    subprocess.run(['nginx', '-s', 'reload'], check=True)

We guarantee 99.99% uptime and provide post-deployment support. Our engineers hold Nginx and HAProxy certifications. Request a consultation right now.

We regularly encounter a situation: "The site is not opening" at 3 a.m. — and it turns out that the VPS disk is full because nginx logs haven't been rotated for six months. Or the server went down under load on the day of an advertising campaign launch because the shared hosting had a limit of 50 concurrent connections. Setting up hosting and deployment is not about "where it's cheaper" but about what happens when something goes wrong. Our team helps avoid such incidents by designing infrastructure that accounts for real load patterns.

When to choose Vercel and Netlify?

Vercel is built for Next.js — deploy in one push, preview deployments for every PR, automatic CDN, Edge Functions, ISR without configuration. For frontend projects and JAMstack, it's the optimal choice: no operational overhead, time-to-deploy measured in minutes.

Real limitations: Vercel Serverless Functions run in us-east-1 by default (latency for Europe +80–100ms), Function timeout 300 seconds on Pro, Bandwidth 1TB/month on Pro. For heavy backend, you need workers or a separate server.

Netlify is closer to static sites and Edge Functions based on Deno Deploy. Build minutes are the main limitation on the free tier.

Why is Docker the foundation of predictable deployment?

"It works on my machine" — classic. Docker solves this through environment containerization. But a bad Dockerfile creates new problems.

A typical mistake: copying everything into the image without .dockerignore, resulting in an 800MB image instead of 80MB. node_modules inside the image weighs as much. Correct approach: multi-stage build.

FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

FROM node:20-alpine AS runner
WORKDIR /app
COPY --from=builder /app/.next ./.next
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./package.json
EXPOSE 3000
CMD ["npm", "start"]

Final image: 180MB instead of 1.2GB. CI build time is reduced due to layer caching — if package.json hasn't changed, the layer with npm ci is taken from cache.

Docker Compose for local development and simple production scenarios: application + PostgreSQL + Redis in one configuration. For production on a single server, it's a perfectly viable option if there's no requirement for horizontal scaling.

More about containerization — Wikipedia: Docker.

How to set up Nginx as a reverse proxy?

Nginx in front of the application is standard for VPS and dedicated servers. Main functions: SSL termination, gzip, static files, rate limiting, upstream load balancing.

A configuration often done incorrectly: worker_processes auto — number of processes equals CPU count. worker_connections 1024 — that's 1024 per worker process. With 4 CPUs and 1024 connections = 4096 concurrent connections. For a high-traffic site, you need worker_connections 4096 and set keepalive_timeout 65.

For static assets with hash in the filename:

location ~* \.(js|css|woff2|png|webp)$ {
    expires 1y;
    add_header Cache-Control "public, immutable";
}

immutable tells the browser: don't revalidate this file even on hard refresh. This only works correctly with content-hashed filenames (which Vite/webpack do by default). Documentation — Wikipedia: Nginx.

AWS: flexibility and complexity

EC2 + Auto Scaling Group — classic for horizontal scaling. AMI with pre-installed application, Launch Template, ASG with min/desired/max instances, Application Load Balancer. When CPU > 70% for 3 minutes — scale out, when CPU < 30% for 15 minutes — scale in. Health check via ALB removes unhealthy instances from rotation.

ECS Fargate — containers without managing EC2. Deploy a Docker image, specify CPU/memory (512 CPU units = 0.5 vCPU, from 512MB memory), Fargate launches it. More expensive than Lambda, but no cold start and no timeout limitations. Suitable for long-running processes, WebSocket servers, heavy workers.

RDS for PostgreSQL with Multi-AZ: automatic failover in 1–2 minutes when primary fails. Read Replicas for scaling reads. RDS Proxy for connection pooling — Lambda functions cannot hold long-term connections, the proxy buffers this.

Kubernetes: when it is justified

K8s adds significant operational complexity. Justified when: multiple teams deploy independent services, fine-grained resource allocation per service is needed, canary deployments and blue/green without downtime are required.

AWS EKS, GKE, or managed k8s from Hetzner (cheaper). Helm charts for standard services. Horizontal Pod Autoscaler based on CPU and custom metrics (RPS via Prometheus).

For most startups and medium-sized projects, Kubernetes is overkill. ECS or Fly.io provide 80% of the capabilities with 20% of the operational complexity.

Monitoring and alerting

A server without monitoring is waiting for an incident. Minimal stack: Prometheus + Grafana (or Grafana Cloud for managed), alerting on disk > 80%, memory > 85%, CPU > 90% over 5 minutes, error rate > 1%. Uptime via Better Uptime or Upptime (self-hosted).

Logs: Loki + Grafana or CloudWatch Logs Insights. Structured JSON logs (winston, pino) are mandatory — otherwise, log searching becomes a pain.

What is included in hosting setup

• Audit of current infrastructure and load profiling
• Selection of target architecture (VPS, AWS, serverless, Kubernetes)
• Setting up CI/CD pipeline (GitHub Actions, GitLab CI) with automatic deployment
• IaC via Terraform or Pulumi (infrastructure as code)
• Configuration of Nginx, SSL certificates, HTTP/2, brotli
• Monitoring and alerting (Prometheus + Grafana, PagerDuty)
• Documentation of runbooks and team training

Additionally, contact us if you need migration from current hosting or integration with external services.

Work process

1. Audit of current infrastructure (2–5 days)
2. Selection of target architecture with load and budget justification (1–3 days)
3. Setting up CI/CD pipeline (GitHub Actions, GitLab CI) (2–5 days)
4. IaC via Terraform or Pulumi (3–10 days)
5. Setting up monitoring and alerting (2–5 days)
6. Documentation of runbooks and team training (1–3 days)

Our experience — 7 years on the market, over 50 projects, guarantee of operability after deployment.

Timeline

• Basic deployment on VPS with Docker + Nginx + CI/CD: 1–2 weeks.
• Setting up AWS infrastructure with Auto Scaling, RDS, CDN: 3–6 weeks.
• Migration to EKS from scratch: 6–12 weeks.
• Setting up Vercel/Netlify for JAMstack: 3–5 days.

The cost is calculated individually depending on complexity and scope of work. Get a consultation — we'll evaluate your architecture in one day.